A New Fractional-Order Mask for Image Edge Detection Based on Caputo–Fabrizio Fractional-Order Derivative Without Singular Kernel

Lavín-Delgado, J. E.; Solís-Pérez, J.E.; Gómez-Aguilar, J. F.; Escobar-Jiménez, R.F.

doi:10.1007/s00034-019-01200-3

Cited by 31 publications

(12 citation statements)

References 32 publications

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“…Another interesting novel finding by Delgado shows that edge detection could be done without a fixed kernel. A non-local fractional mask is designed, the parameters take account of visual perception and its statistical analysis, allowing it to extract more fine image details from overall spatial information [25]. Besides, there is a study on edge detection in the frequency domain with the framework that connects Ridgelet transform, Bezier curve, and Sobel operator.…”

Section: Related Workmentioning

confidence: 99%

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Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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In computer vision, edge detection is a crucial step in identifying the objects' boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true nonedge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.

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Section: Related Workmentioning

confidence: 99%

“…Aneurysms refer to abnormal arterial dilatation and weakening of an artery wall, that can cause rupture and life-threatening internal bleeding [25]. Therefore, early prediction of rupture is needed yet the local inhomogeneous contrast makes the edge detection becomes more challenging [43].…”

Section: Edge Detection On Aneurysms Imagesmentioning

confidence: 99%

Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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“…It shows that the soft computing technique for edge detection gives better results compared to the classical approach. In [10], the authors developed a new fractional-order edge detector via the Caputo-Fabrizio derivative. Using visual perception and statistical analysis, the proposed novel fractional mask without a singular kernel showed significant advantages over other compared methods.…”

Section: Introductionmentioning

confidence: 99%

Asumu Fractional Derivative Applied to Edge Detection on SARS-COV2 Images

Nchama

Alfonso

Morales

et al. 2022

Journal of Applied Mathematics

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Edge detection consists of a set of mathematical methods which identifies the points in a digital image where image brightness changes sharply. In the traditional edge detection methods such as the first-order derivative filters, it is easy to lose image information details and the second-order derivative filters are more sensitive to noise. To overcome these problems, the methods based on the fractional differential-order filters have been proposed in the literature. This paper presents the construction and implementation of the Prewitt fractional differential filter in the Asumu definition sense for SARS-COV2 image edge detection. The experiments show that these filters can avoid noise and detect rich edge details. The experimental comparison show that the proposed method outperforms some edge detection methods. In the next paper, we are planning to improve and combine the proposed filters with artificial intelligence algorithm in order to program a training system for SARS-COV2 image classification with the aim of having a supplemental medical diagnostic.

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“…Voxels can be described as the 3D equivalent of 2D pixels [30]. 2D kernels have been used in image processing [31] and, this paper proposes a way for voxels to be considered as an extension of 2D kernels to 3D space for sensor placement. The specific contributions is a sensor placement algorithm which considers:…”

Section: Introductionmentioning

confidence: 99%

Voxel-based sensor placement for additive manufacturing applications

2021

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The manufacturing sector is experiencing a transformation to Industry 4.0. The authors are researching 3D printable sensors to measure operating conditions like wear, strain and temperature, as part of a project to develop a large-scale multi-material extrusion 3D printer to print a Gravity Separation Spiral (GSS) -a piece of mining equipment that separates minerals from slurry. This paper proposes a sensor placement methodology for placing embedded sensors in large 3D printed objects. Voxels, the 3D equivalent of 2D pixels, are used to discretise the object and an optimisation routine optimally positions the 3D printable sensors into a 3D printed object. The optimisation objectives that are used during sensor placement include the information gain from the sensor, the ability to print the sensor using a robot, and the ability to discourage sensors being placed in important structural locations by penalising these voxels. Finite element analysis is employed to measure the information gain, while the robot arm's manipulability measures the capability to print at each voxel location. The objectives are integrated using 3D kernels, which are represented by voxels shaped in the size of the sensor and different weights related to the intricate traces that need to be printed. Using a weighted objective function the best locations are chosen. A simulation environment has been devel-

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A New Fractional-Order Mask for Image Edge Detection Based on Caputo–Fabrizio Fractional-Order Derivative Without Singular Kernel

Cited by 31 publications

References 32 publications

Mini Kirsch Edge Detection and Its Sharpening Effect

Mini Kirsch Edge Detection and Its Sharpening Effect

Asumu Fractional Derivative Applied to Edge Detection on SARS-COV2 Images

Voxel-based sensor placement for additive manufacturing applications

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